Fuel-efficient sequencing for multi-module spacecraft on-orbit assembly under time constraints via hybrid optimization

Balancing time constraints and fuel efficiency is a critical challenge for rapid on-orbit assembly of large-scale structures. This paper proposes a hierarchical hybrid optimization algorithm to solve fuel-optimal sequencing under strict time constraints. This algorithm integrates the Viterbi algorit...

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Veröffentlicht in:Acta astronautica Jg. 238; S. 202 - 211
Hauptverfasser: Li, Meng, Guo, Ge, Li, Yichao, Li, Xiaoyu, Wu, Chen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.01.2026
Schlagworte:
Fuel-optimal rendezvous sequencing
Hybrid optimization algorithm
Multi-module spacecraft
Orbital perturbations
Rapid assembly strategy
Time constraints
Hybrid optimization algorithm
Multi-module spacecraft
Fuel-optimal rendezvous sequencing
Rapid assembly strategy
Orbital perturbations
Time constraints
ISSN:0094-5765
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Zusammenfassung:Balancing time constraints and fuel efficiency is a critical challenge for rapid on-orbit assembly of large-scale structures. This paper proposes a hierarchical hybrid optimization algorithm to solve fuel-optimal sequencing under strict time constraints. This algorithm integrates the Viterbi algorithm for global sequence planning, particle swarm optimization (PSO) and sequential quadratic programming (SQP) for local orbital maneuver optimization, and the least squares (LS) method for final solution refinement. This hierarchical structure mitigates the limitations of individual algorithms, such as the tendency of PSO to local optima and the high computational complexity of SQP, thereby enhancing both efficiency and accuracy. The proposed strategy enables concurrent maneuver planning and execution for multiple modules, while the final docking operations remain sequential for safety. Simulations under the two-body model demonstrate that the algorithm generates an optimal sequence with a total ΔV of 3432.4 m/s and total assembly time of 57,200 s. Validation under a full perturbation model (including J2, lunisolar gravity, and solar radiation pressure) confirms robustness, with a total ΔV of 3071.6 m/s and time of 53,786.9 s. The method achieves 22.3 % faster computation than Non-dominated Sorting Genetic Algorithm II (NSGA-II) and higher solution stability (standard deviation of 0.027 vs. 0.039). Scalability is validated for 4–16 modules, reducing complexity exponentially in compared with Traversal algorithms. This work provides a rigorous algorithmic framework and a practical strategy reference for fuel-efficient, time-constrained on-orbit assembly of large spacecraft, supporting engineering applications in lunar and deep-space missions. •Hybrid algorithm for fuel-optimal on-orbit assembly under time constraints.•Integrates Viterbi, PSO, SQP, and LS for sequence and maneuver optimization.•Validated under two-body and full perturbation models (J2, lunisolar gravity, SRP).•22.3 % faster computation and higher stability than NSGA-II.•Scalable to 4–16 modules with exponential complexity reduction.
ISSN:0094-5765
DOI:10.1016/j.actaastro.2025.10.009